2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)最新文献

Distributed Radar Network Implementation Using Software Defined Radio 利用软件无线电实现分布式雷达网络

2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2022-05-06 DOI: 10.1109/LISAT50122.2022.9924078

D. Wong, B. K. Chalise, A. Martone, B. Kirk

{"title":"Distributed Radar Network Implementation Using Software Defined Radio","authors":"D. Wong, B. K. Chalise, A. Martone, B. Kirk","doi":"10.1109/LISAT50122.2022.9924078","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9924078","url":null,"abstract":"Target detection capability of a radar system can be significantly enhanced by deploying a distributed network. Despite their potential, distributed radar networks have not received enough attention in the literature. This is attributed, in part, to the need for highly reliable communications links, that can support information sharing between nodes with a low latency, and achieving different levels of synchronization. In this paper, we consider a distributed network with a central coordinator (CC), where nodes, operating under radar phase in different frequency bands, calculate generalized likelihood ratio test (GLRT) function values from their received signals. During communications phase, nodes quantize their GLRT values, encode resulting quantized values to bits, and transmit these bits to the CC using amplitude shift keying (ASK) modulation. The CC demodulates noisy received signals from nodes, decodes resulting information, and estimates GLRT function values transmitted by all nodes. The CC fuses the estimated GLRT values and makes a global decision on target detection. We implement this distributed detection method with a software defined radio (SDR) technique that uses Universal Software Radio Peripheral (USRP) kits from National Instruments (NI) and LabView. Under the setup in which the CC and radar nodes are synchronized with a common reference clock, we demonstrate the key steps for implementing the proposed distributed detection with the SDR approach.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116366745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Designing MADE, an Augmented Reality Application for Macromolecule Visualization 增强现实技术在大分子可视化中的应用

2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2022-05-06 DOI: 10.1109/LISAT50122.2022.9923986

Hyejin Hannah Kum-Biocca, E. Farinas, Nisha Mistry, Rama Chetan Atmudi

{"title":"Designing MADE, an Augmented Reality Application for Macromolecule Visualization","authors":"Hyejin Hannah Kum-Biocca, E. Farinas, Nisha Mistry, Rama Chetan Atmudi","doi":"10.1109/LISAT50122.2022.9923986","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9923986","url":null,"abstract":"The MADE (Molecular Augmented reality for Design and Engineering) application is an augmented reality (AR) tool that affords users the ability to visualize complex macromolecules in an interactive and exploratory environment. The process to develop and design MADE required a thorough analysis of user segments, which informed the commercial prospects of MADE’s smartphone and HoloLens application. To determine the user’s needs and corresponding value propositions, a set of customer discovery interviews were conducted with individuals from MADE’s potential user segments. Through the customer discovery process, active user engagement revealed unique value propositions and helped identify user segments of higher education students and professors and life science professionals. For the aforementioned user segments, the design of the MADE application serves as an educational tool for enhanced learning experience, as well as a collaboration tool for exploratory drug development and discovery. The discovery and design process for MADE revealed viability of the application for commercialization in the higher education and life science profession markets. This paper discusses the processes of research and design of the MADE application.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126375470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Machine Learning Utility for Detection of Potential Protected Health Information Images 用于检测潜在受保护的健康信息图像的机器学习实用程序

2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2022-05-06 DOI: 10.1109/LISAT50122.2022.9924127

Scott J. Vollmin, S. Robila

{"title":"A Machine Learning Utility for Detection of Potential Protected Health Information Images","authors":"Scott J. Vollmin, S. Robila","doi":"10.1109/LISAT50122.2022.9924127","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9924127","url":null,"abstract":"Often, dental x-rays and digital images which may contain sensitive patient information are saved to network and local directories increasing the vulnerability as well as the legal liability of the institutions that process them. To reduce the risk, various approaches are employed by IT staff to detect such images and ensure their proper handling. Searching for images manually is a tedious and time-consuming task; rather automated tools would be preferred. The goal of this project was to investigate how machine learning can be used for automated image recognition as a tool in dental image detection. This paper presents the design, implementation and testing of a user-friendly tool to analyze directories and subdirectories identified by a user to identify and handle Protected Health Information (PHI). The tool provides an interface for a user to select various options for file handling and image search parameters that are then used with a convolutional neural network (CNN) to identify dental radiographs in an arbitrary list of files. Experimental results show that the tool accurately detects potential PHI-related files, leading the way for a practitioner ready deployment.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128250913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Perpendicular lensed optical fibers for biomedical & defense applications: (Exploring the workability of lensed fibers in practical domains) 垂直透镜光纤在生物医学和国防领域的应用:(探索透镜光纤在实际领域的可行性)

2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2022-05-06 DOI: 10.1109/LISAT50122.2022.9923988

Samuel J Kassey, Samson Charles Kassey

引用次数: 0

A Cognitive-Radio Experimental Testbed For Shift-Invariant, Asynchronous Channel-Hopping Sequences With Modern Software-Defined Radios 基于现代软件定义无线电的移动不变异步信道跳频序列认知无线电实验平台

2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2022-05-06 DOI: 10.1109/LISAT50122.2022.9924057

W. Kwong, Alex W. Arthur, Feng-Wen Lo, Guu-chang Yang

{"title":"A Cognitive-Radio Experimental Testbed For Shift-Invariant, Asynchronous Channel-Hopping Sequences With Modern Software-Defined Radios","authors":"W. Kwong, Alex W. Arthur, Feng-Wen Lo, Guu-chang Yang","doi":"10.1109/LISAT50122.2022.9924057","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9924057","url":null,"abstract":"Cognitive radio (CR) is an important wireless technology for contemporary spectrum-demanding applications, such as the fifth-generation and internet-of-things communication systems. The CR technology can dynamically alleviate the common problems of spectrum underutilization and scarcity and thus can optimize spectral efficiency and throughput. Unlicensed users are equipped with CR transceivers to monitor the real-time usage of the licensed channels so that they can “rendezvous” whenever there is any unoccupied channel. This paper reports the implementation of a CR experimental testbed using off-the-shelf modern software-defined radios (SDRs). To support the ad hoc nature of CR wireless communications, a family of shift-invariant (SI), asynchronous channel-hopping (CH) sequences that have the desirable properties of short maximum-time-to-rendezvous and full degree-of-rendezvous are employed in this first-of-its-kind testbed. The hardware set-up and procedures of two SDR experiments are detailed. The throughput performance of these CH sequences in the testbed is modeled, analyzed, and validated by the experimental and simulation results.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121545401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Revisiting Operating System Mass Storage Presumptions Enables Higher Performance and Efficiency 重新访问操作系统大容量存储假设可以提高性能和效率

2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2022-05-06 DOI: 10.1109/LISAT50122.2022.9923959

R. Gezelter

{"title":"Revisiting Operating System Mass Storage Presumptions Enables Higher Performance and Efficiency","authors":"R. Gezelter","doi":"10.1109/LISAT50122.2022.9923959","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9923959","url":null,"abstract":"Random access mass storage has been a pillar of computing since the 1956 delivery of the first IBM 350. The interface paradigm of position then transfer is pervasive, despite exponential advances in access speeds, transfer performance, density, and reliability.Consistency in interface paradigm does not mean that implementations have remained static, there have been numerous improvements: multiple heads, incremental positioning, recording technologies, and storage caches. While multiple levels of storage caches have become ubiquitous, caches are not a panacea. Caches benefit physically sequential usage and repetitive use by reducing physical media accesses. However, systems with hundreds or thousands of actively accessed files can clog caches with data that is only used a single time, yielding cache pollution.Advances in semiconductors have vastly increased processing power and memory capacity, particularly since 1990. Solid state mass storage eliminates rotational delay, but can still have other implementation-related delays. Hierarchical mass storage provides the illusion of transparency, but as with virtual memory, delays attributable to data migration between levels cannot be concealed.Through the 1980s, resource limitations obliged I/O infrastructure implementors to choose minimum resource implementations; other choices were infeasible. Feasibility is mandatory; efficiency and performance are desirable. Better resourced environments allow higher efficiency and performance with some increase in processing and memory consumption.We will examine an approach that preserves the pre-existing I/O API, with minimal culturally-compatible extensions that enable higher performance and increased efficiency.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125590399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multipurpose Solar Charging Station 多功能太阳能充电站

2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2022-05-06 DOI: 10.1109/LISAT50122.2022.9924029

Patrick Evans, Ryan Avery, Maxwell Malcy, Maverick Ruiz, U. Balaji

引用次数: 0

Automated Image Processing Based 3D Printed Scaffolds For Critical Size Bone Fracture Treatment 基于自动图像处理的3D打印支架用于临界尺寸骨折治疗

2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2022-05-06 DOI: 10.1109/LISAT50122.2022.9924121

Abrar Hussain Syed, Ahmadreza Baghaie, A. Ilyas

{"title":"Automated Image Processing Based 3D Printed Scaffolds For Critical Size Bone Fracture Treatment","authors":"Abrar Hussain Syed, Ahmadreza Baghaie, A. Ilyas","doi":"10.1109/LISAT50122.2022.9924121","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9924121","url":null,"abstract":"Bone tissues in critical size bone fracture cases do not re-generate naturally and require special treatment procedures, such as cast alignments, support plates and bone grafting. These procedures are risky and often have high rejection rates. Modern clinical procedures use scaffold implants that can facilitate the process of healing with a lesser risk, provide mechanical support and a porous, nutritious medium for bone tissue regeneration and recovery. They require special training, tools, and significant time to be manufactured, and are generally made at a dedicated laboratory. The whole process takes around a week to be manufactured and implanted in the fracture site. In this work a novel technique for automatic segmentation of bone fractures from CT scan images to facilitate the process of manufacturing the patient-specific scaffolds in a significantly shorter time, without the need of skilled personnel has been presented. To achieve this, the procedure of generating 3D printable models was automated using image processing and machine learning algorithms. For this, 3D CT (Computed Tomography) images were used as input (as a series of 2D slices), acquired using a micro-CT scanner for the approach. After pre-processing the acquired images (filtering), thresholding segmentation was applied to extract the bone from the scan. This step is followed by orientation optimization of the segmentation result, by taking advantage of a global optimization technique, namely Simulated Annealing, to ensure maximized visibility of the fracture in a projected view of the volume by projecting the volume on a 2D surface. Binary hole-filling techniques and bone thickness estimation is then used to create a 3D template (model) to be sent for scaffold printing to a compatible 3D printer. Experiments with both synthetic and real datasets show that the proposed method is an effective approach for creating rapid, precise, and patient-specific 3D scaffolds to treat critical-size bone fractures.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114527591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Unsupervised Machine Learning Approaches to Nuclear Particle Type Classification 核粒子类型分类的无监督机器学习方法

2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2022-05-06 DOI: 10.1109/LISAT50122.2022.9924043

Nicholas Liebers, Jacob Huckelberry, Daniel C Ruiz, D. Fobar, P. Chapman

{"title":"Unsupervised Machine Learning Approaches to Nuclear Particle Type Classification","authors":"Nicholas Liebers, Jacob Huckelberry, Daniel C Ruiz, D. Fobar, P. Chapman","doi":"10.1109/LISAT50122.2022.9924043","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9924043","url":null,"abstract":"Historically, nuclear science and radiation detection fields of research used Pulse Shape Discrimination (PSD) to label gamma-ray and neutron interactions. However, PSD’s effectiveness relies greatly on the existence of distinguishable differences in an interaction’s measured pulse shape. In the fields of machine learning and data analytics, clustering algorithms provide ways to group samples with similar features without the need for labels. Clustering gamma-ray and neutron interactions may mitigate PSD’s pitfalls, since clustering methods view the total waveform rather than just the area under the tail and the total area under the pulse. However, traditional clustering methods, such as the k-means clustering algorithm, suffer from poor performance on high dimensional data. This study explores unsupervised machine learning methods using Deep Neural Networks (DNN) to cluster gamma-ray and neutron interaction measurements collected with an organic scintillation detector, in order to perform binary labeling of gamma-rays and neutrons. Using various network architectures, this research demonstrates the effectiveness of using autoencoder-based neural networks to cluster gamma-ray and neutron interactions when compared to shallow clustering algorithms. The results reveal the effectiveness of autoencoders on high energy gamma-ray and neutron pulses with an energy deposit greater than 0.80 MeVee whilst greatly outperforming k-means comparatively in all cases.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"24 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114028449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Soldier Mounted Advanced Radiation Tracking (SMART) Detector 士兵安装的先进辐射跟踪(SMART)探测器

2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2022-05-06 DOI: 10.1109/LISAT50122.2022.9924048

Vincent Gasparri, E. Gonzalez, Valentina Vincent, W. Koch

{"title":"Soldier Mounted Advanced Radiation Tracking (SMART) Detector","authors":"Vincent Gasparri, E. Gonzalez, Valentina Vincent, W. Koch","doi":"10.1109/LISAT50122.2022.9924048","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9924048","url":null,"abstract":"Nuclear counter proliferation and nuclear material stewardship are critical international processes to maintaining safety and international balance. Monitoring, locating, and investigating special nuclear material is integral to both of these aims. Advanced radiation detection systems contribute to nuclear stockpile safekeeping and prevent unstable military and geopolitical environments. A Time Projection Chamber (TPC) is a common piece of equipment in the world of high energy physics but never used in the realm of nuclear security. A TPC is capable of providing a direction to a source of fast neutrons, a signature unique to spontaneous fission sources and Weapons Grade Plutonium specifically. The ability to collect direction and energy information from a single incident fast neutron is unique to the time projection chamber design. A time projection chamber’s unique data outputs can be used to create a fast neutron detector capable of robust data collection and special nuclear material localization. Previous time projection chamber experimentation and design proves the concept’s effectiveness in lab environments. The Soldier Mounted Advanced Radiation Tracking Detector design uses printed circuit board technology for significant improvements in the field uniformity, allowing for an unprecedented active volume to total volume ratio. The structural improvements will provide the end user with a portable fast neutron detector. Previous work has demonstrated through simulations that a portable TPC can provide a source location accurate to within 60 cm after a single pass with a closest approach of two meters. The improvements discussed in this work result in a single user being capable carrying a TPC while simultaneously collecting data to locate and characterize a 1kg source of WGPu on a single pass from two meters.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121788652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0